Comparison
Awesome-Code-LLM vs llm-pruning-collection
Verdict
Pick Awesome-Code-LLM if awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers; pick llm-pruning-collection if the llm-pruning-collection repository provides a comprehensive set of large language model pruning methods, along with the necessary training and evaluation scripts for GPUs and TPUs.
Markdown twin · Awesome-Code-LLM alternatives · llm-pruning-collection alternatives
GraphCanon updated Sep 9, 2026
8views this month
vs
Trust & integrity
| Signal | Awesome-Code-LLM | llm-pruning-collection |
|---|---|---|
| Maintenance | Dormant (635d since push) As of Sep 6, 2026 · github_public_v1 | Slowing (141d since push) As of Sep 9, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 6, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 9, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 15, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | No lockfile (source not queried) As of Aug 23, 2026 · deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | No public record from this source As of Aug 9, 2026 · openssf-scorecard@v1 |
Tagline
- Awesome-Code-LLM
- 👨💻 An awesome and curated list of best code-LLM for research.
- llm-pruning-collection
- Collection of LLM pruning methods and training code for GPUs & TPUs.
Stars
- Awesome-Code-LLM
- 1.3k
- llm-pruning-collection
- 72
Forks
- Awesome-Code-LLM
- 75
- llm-pruning-collection
- 9
Open issues
- Awesome-Code-LLM
- 5
- llm-pruning-collection
- 2
Language
- Awesome-Code-LLM
- -
- llm-pruning-collection
- Python
Adopt for
- Awesome-Code-LLM
- Awesome-Code-LLM is a curated repository focused on code-focused large language models (code-LLMs), providing insights into top-performing models, evaluation toolkits, and research papers.
- llm-pruning-collection
- The llm-pruning-collection repository provides a comprehensive set of large language model pruning methods, along with the necessary training and evaluation scripts for GPUs and TPUs.
Persona
- Awesome-Code-LLM
- -
- llm-pruning-collection
- -
Runtime
- Awesome-Code-LLM
- -
- llm-pruning-collection
- -
License
- Awesome-Code-LLM
- MIT License: Permissive open-source license that allows usage in virtually any project with little restrictions.
- llm-pruning-collection
- Apache-2.0
Last pushed
- Awesome-Code-LLM
- Dec 10, 2024
- llm-pruning-collection
- Apr 20, 2026
Categories
- Awesome-Code-LLM
- Evaluation & Observability, LLM Frameworks
- llm-pruning-collection
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- Awesome-Code-LLM
- Dormant (18%)
- llm-pruning-collection
- Slowing (36%)
Days since push
- Awesome-Code-LLM
- 635d
- llm-pruning-collection
- 141d
Open issues (now)
- Awesome-Code-LLM
- 5
- llm-pruning-collection
- 2
Stars delta
- Awesome-Code-LLM
- -1 (30d)
- llm-pruning-collection
- +3 (30d)
Open issues delta
- Awesome-Code-LLM
- +1 (30d)
- llm-pruning-collection
- 0 (30d)
Owner type
- Awesome-Code-LLM
- User
- llm-pruning-collection
- Organization
deps.dev advisories
- Awesome-Code-LLM
- Not queried
- llm-pruning-collection
- No lockfile (source not queried)
OpenSSF Scorecard
- Awesome-Code-LLM
- Not queried
- llm-pruning-collection
- No public record from this source
Full report
- Awesome-Code-LLM
- Trust report
- llm-pruning-collection
- Trust report
Choose Awesome-Code-LLM if…
- License: Awesome-Code-LLM is MIT, llm-pruning-collection is Apache-2.0.
- Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs..
- Tags unique to Awesome-Code-LLM: awesome, code generation, large language models.
- Also covers LLM Frameworks.
- When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval.
When NOT to use Awesome-Code-LLM
- When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision.
- If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality.
- In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering
Choose llm-pruning-collection if…
- License: llm-pruning-collection is Apache-2.0, Awesome-Code-LLM is MIT.
- Pricing: The software is free and open-source, licensed under Apache-2.0, but users must provide their own hardware or use cloud services like Google TPU Research Cloud for computational resources..
- Requirements: The repository includes pretraining and fine-tuning scripts for both GPU and TPU platforms.; A JAX-based environment is required to run the code in this repository..
- Tags unique to llm-pruning-collection: jax, llm-evaluation, llm-training, pruning.
- Also covers Model Training.
- When you are working on reducing the size or improving inference speed of large language models using various pruning techniques available in this collection.
When NOT to use llm-pruning-collection
- Avoid if your project requires a pruning method that is not included in the collection or if the current platform capabilities do not align with your hardware requirements.
- Not suitable for those who need tools to train models from scratch rather than focusing on model pruning and optimization techniques.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (huybery/Awesome-Code-LLM) · observed Sep 6, 2026
- GitHub forks (huybery/Awesome-Code-LLM) · observed Sep 6, 2026
- Last push (huybery/Awesome-Code-LLM) · observed Dec 10, 2024
- License file (MIT) · observed Sep 6, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zlab-princeton/llm-pruning-collection) · observed Sep 9, 2026
- GitHub forks (zlab-princeton/llm-pruning-collection) · observed Sep 9, 2026
- Last push (zlab-princeton/llm-pruning-collection) · observed Apr 20, 2026
- License file (Apache-2.0) · observed Sep 9, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: Awesome-Code-LLM 1.3k · llm-pruning-collection 72 (synced Sep 6, 2026).
Common questions
- What is the difference between Awesome-Code-LLM and llm-pruning-collection?
- Awesome-Code-LLM: 👨💻 An awesome and curated list of best code-LLM for research.. llm-pruning-collection: Collection of LLM pruning methods and training code for GPUs & TPUs.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Code-LLM over llm-pruning-collection?
- Choose Awesome-Code-LLM over llm-pruning-collection when License: Awesome-Code-LLM is MIT, llm-pruning-collection is Apache-2.0; Requirements: No specific requirements to use the repository for reference or evaluation, but contributions may involve technical knowledge and familiarity with code-LLMs.; Tags unique to Awesome-Code-LLM: awesome, code generation, large language models; Also covers LLM Frameworks; When you need a comprehensive list of state-of-the-art code generation LLMs with performance metrics such as HumanEval.
- When should I choose llm-pruning-collection over Awesome-Code-LLM?
- Choose llm-pruning-collection over Awesome-Code-LLM when License: llm-pruning-collection is Apache-2.0, Awesome-Code-LLM is MIT; Pricing: The software is free and open-source, licensed under Apache-2.0, but users must provide their own hardware or use cloud services like Google TPU Research Cloud for computational resources.; Requirements: The repository includes pretraining and fine-tuning scripts for both GPU and TPU platforms.; A JAX-based environment is required to run the code in this repository.; Tags unique to llm-pruning-collection: jax, llm-evaluation, llm-training, pruning; Also covers Model Training; When you are working on reducing the size or improving inference speed of large language models using various pruning techniques available in this collection.
- When should I avoid Awesome-Code-LLM?
- When looking for a tool that provides pre-trained models with built-in APIs or services, as Awesome-Code-LLM is primarily a directory/collection of information without direct service provision. If you require real-time interactive use-cases and need immediate API access to LLMs; this repository does not offer such functionality. In scenarios where you need a single end-to-end solution for training your own code generation models, as the platform is focused on aggregating third-party resources and research rather than offering
- When should I avoid llm-pruning-collection?
- Avoid if your project requires a pruning method that is not included in the collection or if the current platform capabilities do not align with your hardware requirements. Not suitable for those who need tools to train models from scratch rather than focusing on model pruning and optimization techniques.
- Is Awesome-Code-LLM or llm-pruning-collection more popular on GitHub?
- Awesome-Code-LLM has more GitHub stars (1,290 vs 72). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Code-LLM and llm-pruning-collection open source?
- Yes - both are open-source projects on GitHub (Awesome-Code-LLM: MIT, llm-pruning-collection: Apache-2.0).
- Where can I find alternatives to Awesome-Code-LLM or llm-pruning-collection?
- GraphCanon lists graph-backed alternatives at Awesome-Code-LLM alternatives and llm-pruning-collection alternatives (Awesome-Code-LLM markdown twin, llm-pruning-collection markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, Awesome-Code-LLM or llm-pruning-collection?
- Awesome-Code-LLM: Dormant. llm-pruning-collection: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for Awesome-Code-LLM and llm-pruning-collection?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Code-LLM trust report; llm-pruning-collection trust report.